Learning control of a robot manipulator based on a decentralized position-dependent PID controller

2017 ◽  
Author(s):  
Jan Cvejn ◽  
Jiri Tvrdik
Keyword(s):  
Pid Controller ◽  
Robot Manipulator ◽  
Learning Control

On the learning control of a robot manipulator

1991 ◽  
Vol 4 (1) ◽  
pp. 43-53 ◽  
Author(s):  
D. M. Dawson ◽  
Z. Qu ◽  
J. F. Dorsey ◽  
F. L. Lewis
Keyword(s):  
Robot Manipulator ◽  
Learning Control

Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

1990 ◽  
Vol 2 (4) ◽  
pp. 273-281 ◽  
Author(s):  
Masatoshi Tokita ◽  
◽  
Toyokazu Mitsuoka ◽  
Toshio Fukuda ◽  
Takashi Kurihara ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Force Control ◽  
Pid Controller ◽  
Robot Manipulator ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
The Neural Network ◽  
The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

Experiment Verification and Stability Analysis of Iterative Learning Control for Shape Memory Alloy Wire

2019 ◽  
Vol 31 (4) ◽  
pp. 583-593
Author(s):  
Hitoshi Kino ◽  
Naofumi Mori ◽  
Shota Moribe ◽  
Kazuyuki Tsuda ◽  
Kenji Tahara ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Iterative Learning Control ◽  
Robot Manipulator ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Methods ◽  
Experiment Verification ◽  
And Control

To achieve the control of a small-sized robot manipulator, we focus on an actuator using a shape memory alloy (SMA). By providing an adjusted voltage, an SMA wire can itself generate heat, contract, and control its length. However, a strong hysteresis is generally known to be present in a given heat and deformation volume. Most of the control methods developed thus far have applied detailed modeling and model-based control. However, there are many cases in which it is difficult to determine the parameter settings required for modeling. By contrast, iterative learning control is a method that does not require detailed information on the dynamics and realizes the desired motion through iterative trials. Despite pioneering studies on the iterative learning control of SMA, convergence has yet to be proven in detail. This paper therefore describes a stability analysis of an iterative learning control to mathematically prove convergence at the desired length. This paper also details an experimental verification of the effect of convergence depending on the variation in gain.

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